Gene-level association analysis of systemic sclerosis: A comparison of African-Americans and White populations

All authors: Olga Y. Gorlova , Yafang Li, Ivan Gorlov, Jun Ying, Wei V. Chen, Shervin Assassi, John D. Reveille, Frank C. Arnett, Xiaodong Zhou, Lara Bossini-Castillo, Elena Lopez-Isac, Marialbert Acosta-Herrera, Peter K. Gregersen, Annette T. Lee, Virginia D. Steen, Barri J. Fessler, Dinesh Khanna, Elena Schiopu, Richard M. Silver, Jerry A. Molitor, Daniel E. Furst, Suzanne Kafaja, Robert W. Simms, Robert A. Lafyatis, Patricia Carreira, Carmen Pilar Simeon, Ivan Castellvi, Emma Beltran, Norberto Ortego, Christopher I. Amos, Javier Martin, Maureen D. Mayes.Data Availability Statement: Genetic data is available from dbGaP repository (https://www.ncbi. nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_ id=phs000357.v1.p1).Gene-level analysis of ImmunoChip or genome-wide association studies (GWAS) data has not been previously reported for systemic sclerosis (SSc, scleroderma). The objective of this study was to analyze genetic susceptibility loci in SSc at the gene level and to determine if the detected associations were shared in African-American and White populations, using data from ImmunoChip and GWAS genotyping studies. The White sample included 1833 cases and 3466 controls (956 cases and 2741 controls from the US and 877 cases and 725 controls from Spain) and the African American sample, 291 cases and 260 controls. In both Whites and African Americans, we performed a gene-level analysis that integrates association statistics in a gene possibly harboring multiple SNPs with weak effect on disease risk, using Versatile Gene-based Association Study (VEGAS) software. The SNP-level analysis was performed using PLINK v.1.07. We identified 4 novel candidate genes (STAT1, FCGR2C, NIPSNAP3B, and SCT) significantly associated and 4 genes (SERBP1, PINX1, TMEM175 and EXOC2) suggestively associated with SSc in the gene level analysis in White patients. As an exploratory analysis we compared the results on Whites with those from African Americans. Of previously established susceptibility genes identified in Whites, only TNFAIP3 was significant at the nominal level (p = 6.13x10-3) in African Americans in the gene-level analysis of the ImmunoChip data. Among the top suggestive novel genes identified in Whites based on the ImmunoChip data, FCGR2C and PINX1 were only nominally significant in African Americans (p = 0.016 and p = 0.028, respectively), while among the top novel genes identified in the gene-level analysis in African Americans, UNC5C (p = 5.57x10-4) and CLEC16A (p = 0.0463) were also nominally significant in Whites. We also present the gene-level analysis of SSc clinical and autoantibody phenotypes among Whites. Our findings need to be validated by independent studies, particularly due to the limited sample size of African Americans.Funding was provided to MDM by the National Institutes of Health (NIH) the National Institute of Arthritis, Musculoskeletal and Skin Diseases (NIAMS https://www.niams.nih.gov/) Centers of Research Translation (CORT) P50-AR054144, NIH grant N01-AR-02251 and R01-AR-055258, and the Department of Defense (DD) Congressionally Directed Medical Research Program (http://cdmrp.army.mil/) W81XWH-07-1-011 and WX81XWH-13-1-0452 for the collection, analysis and interpretation of the data

Parameters for Medium- and Low-Risk Models in simulation.

<p>OD_hetero: odds ratio for heterozygous genotypes; OD_homo: odds ratio for homozygous genotypes; Add: additive; Dom: dominant; Rec: recessive.</p>a<p>Disease model 1 in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0046612#pone-0046612-g001" target="_blank">Figure 1</a>–<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0046612#pone-0046612-g002" target="_blank">2</a>.</p>b<p>Disease model 3 in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0046612#pone-0046612-g001" target="_blank">Figure 1</a>–<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0046612#pone-0046612-g002" target="_blank">2</a>.</p

Genetic Association Analysis of Complex Diseases Incorporating Intermediate Phenotype Information

<div><p>Genetic researchers often collect disease related quantitative traits in addition to disease status because they are interested in understanding the pathophysiology of disease processes. In genome-wide association (GWA) studies, these quantitative phenotypes may be relevant to disease development and serve as intermediate phenotypes or they could be behavioral or other risk factors that predict disease risk. Statistical tests combining both disease status and quantitative risk factors should be more powerful than case-control studies, as the former incorporates more information about the disease. In this paper, we proposed a modified inverse-variance weighted meta-analysis method to combine disease status and quantitative intermediate phenotype information. The simulation results showed that when an intermediate phenotype was available, the inverse-variance weighted method had more power than did a case-control study of complex diseases, especially in identifying susceptibility loci having minor effects. We further applied this modified meta-analysis to a study of imputed lung cancer genotypes with smoking data in 1154 cases and 1137 matched controls. The most significant SNPs came from the <em>CHRNA3-CHRNA5-CHRNB4</em> region on chromosome 15q24–25.1, which has been replicated in many other studies. Our results confirm that this <em>CHRNA</em> region is associated with both lung cancer development and smoking behavior. We also detected three significant SNPs—rs1800469, rs1982072, and rs2241714—in the promoter region of the <em>TGFB1</em> gene on chromosome 19 (<em>p</em> = 1.46×10⁻⁵, 1.18×10⁻⁵, and 6.57×10⁻⁶, respectively). The SNP rs1800469 is reported to be associated with chronic obstructive pulmonary disease and lung cancer in cigarette smokers. The present study is the first GWA study to replicate this result. Signals in the 3q26 region were also identified in the meta-analysis. We demonstrate the intermediate phenotype can potentially enhance the power of complex disease association analysis and the modified meta-analysis method is robust to incorporate intermediate phenotype or other quantitative risk factor in the analysis.</p> </div

Power Plots for the Medium-Risk Model.

<p>Power Plots for the Medium-Risk Model.</p

Three possible disease models for one disease locus with an intermediate phenotype.

<p>G, disease susceptibility locus; D, disease status; QT, quantitative trait (an intermediate phenotype).</p

Manhattan Plot of GWA Studies of Lung Cancer and CPD Data.

<p>1–4: case-control method (λ = 1.018), linear regression analysis with adjustment for disease status (λ = 1.010), Fisher's combined probability test (λ = 1.013), and the modified inverse-variance weighted method (λ = 1.011). −log₁₀(p)>4.5 was used as the cutoff in plot 1 to match with the previous GWA study published in 2008 (Nat Genet, 40.5: 616–622). −log₁₀(p)>5 was used as the cutoff in plot 2–4 to reduce false discovery rate.</p

<i>P</i>-Value Comparisons between the tests.

<p>X-axis, −log₁₀(p) from logistic regression analysis. Y-axis, −log₁₀(p) from Fisher's combined probability test (left); −log₁₀(p) from the modified inverse-variance weighted method (right).</p

Power Plots for the Low-Risk Model.

<p>Power Plots for the Low-Risk Model.</p

−Log₁₀(P) Plot of Significant SNPs on Chromosomes 3, 15, and 19 in Meta-analysis of imputed lung cancer genotypes with smoking data.

<p>−Log₁₀(P) Plot of Significant SNPs on Chromosomes 3, 15, and 19 in Meta-analysis of imputed lung cancer genotypes with smoking data.</p

Additional file 1: of Combination therapy with protein kinase inhibitor H89 and Tetrandrine elicits enhanced synergistic antitumor efficacy

Supplementary figures and figure legends. (DOCX 1246 kb